The classroom response system based on affective computing

In recent years, school teachers tend to use the applications of Information and Communication Technologies that are developing rapidly and universally to promote interaction between students and teachers for effective learning. Classroom response systems can effectively capture the learning outcomes of students in the classroom, and the outcomes will feedback the teachers immediately to improve their teaching. However, the existing classroom response systems have to get the learning outcome through testing students from curse, but learners´ autonomy often impact the learning outcomes; if learners are absent-mind or do not pay attention to the question described at a second and do not press the correct button, the result cannot be effective. This paper proposes a new classroom response system, and this system will be used to achieve the effectiveness of learning through the test (the level of understanding, namely); the same time when using webcams to record learner´s facial expressions, the characteristic of facial expressions will be captured and analyzed. Then the system will use the cat swarm optimization and support vector machines to identify the level of understanding associated with the expression characteristics and classification model. The experiment results demonstrate the selected 9 face expressions and verify the 100% classification accuracy of the proposed system.